Spectral imaging is a technique for generating a spatial map of the wavelength variations of light from a scene. It has found use in many applications, such as environmental sensing, military and civilian surveillance, homeland security, military target discrimination, astrophysics, metrology, and biomedical imaging.
The spatial map generated using spectral imaging is often referred to as a spatio-spectral datacube. Such a datacube comprises a dataset of pixels (referred to as “voxels”), each which is characterized by a two-dimensional spatial coordinate and a spectral coordinate. Typically, a spatio-spectral datacube is developed for several wavelengths (a.k.a., spectral components) of interest.
Several techniques for spectral imaging have been developed, including tomographic imaging, pushbroom imaging, and snap-shot imaging.
Tomographic spectral imaging develops a datacube by sequentially forming images at each of a several wavelengths. Typically, a dispersive element, such as a prism or diffraction grating, is rotated through a series of positions to spread the spectral components at a plurality of directions onto the photodetector array. At each position, the photodetector array generates a spatial map of the overlapped spatial and spectral voxels of a scene. A processor then compiles all of the individual spatial-spectral maps into a spectral datacube.
The optical efficiency of a tomographic imager is typically quite high; therefore, such imagers are useful in low-light applications. Unfortunately, tomographic spectral imagers are very slow since each image must be acquired while the scene is substantially static. Further, the geometry of such systems normally limits the range of angles over which the dispersive element can be rotated and, therefore, the number of spectral components that can be included in the datacube.
Pushbroom spectral imagers (and related techniques such as whisk broom imagers and tunable filter imagers) develop a datacube by capturing a one- or two-dimensional subset of the datacube and then temporally scanning to obtain the remaining dimension(s). Such imagers typically require high light input and have very poor signal-to-noise ratios. Further, such imagers are not particularly applicable for imaging non-static scenes.
A number of snap-shot imagers have been developed to overcome many of the limitations of tomographic and temporally scanned imagers. A snap-shot imager provides all of the information of the data cube to a photodetector array at one time; however, the information is multiplexed over the array of photodetectors. One such conventional snap-shot imager, often referred to as a “coded-aperture snap-shot imager (CASSI),” employs a coded aperture and one or more dispersive elements to modulate the multi-spectral optical field received from a scene. In a CASSI system, a photodetector array receives the modulated optical field as a single two-dimensional projection of the scene, where each pixel of the photodetector array measures light of one of the plurality of spatial-spectral components of the datacube. The manner in which the multiple projections are multiplexed is dependent upon the design of the coded aperture and the relative position of the coded aperture and the dispersive elements.
While a snap-shot imager acquires all of the desired spectral information of a scene simultaneously, the unraveling (i.e., demultiplexing) the multiple projections to assemble the datacube can be quite computationally complex. As a result, spectral image computation time can be time-consuming. This limits the operation rate for such spectral imagers, which precludes their use in many high-speed imaging applications.